Journal of Intelligent & Fuzzy Systems - Volume 34, issue 5

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ISSN 1064-1246 (P)
ISSN 1875-8967 (E)

Impact Factor 2018:1.426

The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.

The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.

Abstract: Recently, available data has increased explosively in both number of samples and dimensionality. The huge number of high dimensional data generates the presence of noisy, redundant and irrelevant dimensions. Such dimensions can increase the time and computational cost in the learning process and even degenerate the performance of learning tasks. One of the ways to reduce dimensionality is by Feature Selection (FS). The aim of this paper is study the feature selection based on expert knowledge and traditional methods (filter, wrapper and embedded) and analyze their performance in classification tasks. Three datasets related to cancer domain in humans were used…for feature selection: Breast Cancer (BC), Primary Tumor (PT) and Central Nervous System (CNS). C4.5, K-Nearest Neighbors, Support Vector Machine and Multi Layer Perceptron were trained with the best subset of features for each cancer dataset. The subset of features selected by the wrapper method presents the best average accuracy in the datasets BC and PT, while the subset of features selected by the embedded method reaches the highest average accuracy in the CNS dataset.
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Abstract: Social networks users often post their opinion after reading a news article. By analyzing these responses, it is possible to find diverse emotions expressed in them. When several users react to an article, a distribution of these emotions is accumulated. Writers and publishers would benefit to have an estimation of how users will react to an article. This work proposes a method to predict the distribution of emotions that users would express in Twitter after reading a news article. More than one emotion can be expressed in responses, so that an approach of modeling this distribution as a supervised multi-target…classification problem is followed. For this purpose, it was necessary to collect a corpus of Spanish news articles and their associated responses and a group of annotators tagged the emotions expressed in them. The use of this strategy allows to naturally model instances (news articles) that have more than one associated class (emotions expressed in responses). The predicted values are expressed in terms of the percentage of responses that triggered each specific emotion. The proposed method is evaluated by measuring the deviation of the predicted emotion distribution with regard to the annotated set of emotions, obtaining a precision above 90%. In addition to that, the proposed method was used in a foreign corpus in order to compare it with 10 state of the art methods. Results show that the proposed method performs better than 9 of these methods on this corpus.
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Abstract: Actionable Patterns are desired knowledge to be mined from large datasets. Action Rules are vital data mining method for gaining actionable knowledge from the datasets. They recommend actions which users can undertake to their advantage, or to accomplish their goal. Meta actions are the sub-actions to the Action Rules, which intends to change the attribute value of an object, under consideration, to attain the desirable value. The essence of this paper is to propose a new optimized and more promising system, in terms of speed and efficiency, for generating meta-actions by implementing Specific Action Rule discovery based on Grabbing strategy…(SARGS) algorithm, and to apply that for Sentiment Analysis on Twitter data. We perform a comparative analysis of meta-actions generating algorithmic implementation in Apache Spark driven system, conventional Hadoop driven system and Single node machine using the Twitter social networking data and evaluate the results. We implement corpus based Sentimental Analysis of social networking data, and test the total time taken by the systems and their sub components for the data processing. Results show faster computational time for Spark system compared to Hadoop MapReduce and Single node machine for the meta-action generation methods.
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Abstract: Given the immediacy of social networks, commonly called sociodigital networks, it is necessary to develop methods to retrieve and interpret visually and in an organized way large amounts of information. Although there are tools that classify the information by using a visualization, generally in form of graphs, the identification of the topics around an event remains complicated. This article describes the use of dendrograms, as a different visual representation, by analyzing the frequency of the terms used in the tweets as well as the relationship between them. Thus, the use of semantic dendrograms facilitates the immediate identification of themes and…subtopics of a given event by showing a clustering of these in the form of a tree.
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Abstract: We explore various machine learning-based classifiers applied to rule-based features for recognizing textual entailment. The features, extracted with a set of synthesized matching rules, reflect syntactic and semantic similarity between the text and the hypothesis. The fact that we use only seven relatively simple features makes our method suitable for low-resource languages. We test our method on the test sets of the RTE competitions and achieve accuracy of up to 69.13%.

Abstract: In this paper, the use of collection term frequencies (i.e. the total number of occurrences of a term in a document collection) in the BM25 retrieval model is investigated by modifying its term frequency (TF) and inverse document frequency (IDF) components. Using selected examples extracted from TREC collections, it was observed that the informative nature, for retrieval purposes, of terms, either with the same TF (in a document) or IDF (in a collection) may be better revealed with the use of collection term frequencies (CTF). From three new heuristics based on those observations and deviations from a random Poisson model,…collection term frequencies were integrated to TF and IDF factors. The novel formulations were tested by employing the TREC-1 to TREC-8 collections in the ad hoc task, for which BM25 was first developed and tested. Consistent and significant improvements were observed in mean average precision (MAP) reaching up to 17.67% for the TREC-8 dataset, and 7.16% averaged over all tested collections. These results were considerably better in comparison to other approaches surveyed aiming to improve BM25, proving in this way the effectiveness of the proposed heuristics and formulae. The proposed approach requires only additional offline pre-computations and does not entail extra computational complexity for retrieval while keeping the original spirit and parameter robustness of BM25.
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Abstract: Text Lines Segmentation (TLS) affects the performance of Manuscript Text Recognition (MTR) systems from document images. At the same time, the TLS task consists of two tasks: the first is Text Lines Localization (TLL) and the second is the Search of the Path that Divides neighboring Lines (SPDL) of handwritten text. The TLS task depends on the type of language, author’s writing style, pen type and document quality. In this paper, Projected Energy Map with Alpha blending (PEM-Alpha) is presented as an unsupervised method for the TLL task, which can work with lines that are touching or overlapping. In addition,…SPDL-GA is proposed as a method for SPDL task which finds the line that best splits the text. The experimentation is carried out with a standard collection of historical multilingual documents. Through experimentation it is demostrated that the proposed methods outperform other state-of-the-art methods, even in documents with mixed languages. In addition, few parameters required by PEM-Alpha and SPDL-GA are automatically calculated.
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Abstract: This work highlights how to transform information from invoice documents to semantic models, as an implementation of ontology modeling. The migration from printed paper to digital documents in the Mexican Government Offices in the last few years has brought significant opportunities for the usage of information technologies and applications. However, when changing digital document information into knowledge, there are still many gaps to be filled. This work proposes a solution to some issues regarding ontology modeling, specifically when mapping a document that follows some XML schema to an ontology under the OWL standard. The main contribution of this work is…to provide new interpretations of the XML terms in the context of OWL, so that the XML Schema Definition (XSD) structures can be mapped into more complex OWL structures. A software tool developed to test and validate the information extraction strategies proposed is presented here.
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Abstract: In supervised classification, a training set is given to a classifier to learn a decision rule for classifying unseen cases. When large training sets are processed, the training stage becomes slow especially for instance-based learning. However, not all information in a training set is useful for classification because it could contain either redundant or noisy prototypes. Therefore a process for discarding useless prototypes is required; this process is known as prototype selection. In this work, we present some methods for selecting prototypes based on prototype relevance, which are accurate and fast for large datasets; in addition, our methods can be…applied over datasets described by nominal features. We report experimental results showing the effectiveness of our methods as well as a comparison against other successful prototype selection methods.
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Abstract: Recently, the extraction of clinical events from unstructured medical texts has attracted much attention of the research community. Machine learning approaches are popular for this task, due to their ability to solve the problem of sequence tagging effectively. It has been suggested previously that simple features, such as word unigrams, part-of-speech tags, chunk tags, among others, are sufficient for this task. We show that more careful preprocessing and feature selection can significantly improve the results. We used conditional random field classifier with more linguistically oriented features and outperformed the current state-of-the-art approaches. We also show that the popular and much…simpler Viterbi algorithm (hidden Markov model-based classification algorithm) can produce competitive results, when its parameters are tuned using specific optimization techniques. We evaluate these algorithms for the task of extraction of medical events from the corpus developed for SemEval shared Task 12: Clinical TempEval (Temporal Evaluation) 2016, namely, for its two subtasks: (i) event detection and (ii) event classification based on contextual modality.
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